1. Field of the Invention
This invention relates to structures and algorithms for generating and receiving signals for communications, surveillance, and navigation.
2. Description of Related Art and General Background
Applications for Noise-like Signal
In certain wireless communications, surveillance, and navigation (CSN) applications, it is desirable to transmit a signal such that an unintended recipient would perceive the signal as no more than background noise (as discussed in references SD1-SD3, which documents are hereby incorporated by reference). One such application is covert communications systems, wherein a signal disguised as noise becomes harder for a curious interloper to detect. Such signals are said to exhibit a xe2x80x98low probability of detectionxe2x80x99 (LPD). Another such application is multiple access systems, wherein it is theorized that the interference caused by other users"" signals would be reduced by making the signals more noise-like.
Transmit Issues
In covert communications systems, the object is to communicate in such a manner that an unfriendly party will be unable to detect the presence of the communications signal. While low power techniques for such communications exist, they involve an obvious and unavoidable tradeoff between evading detection and maintaining a robust communications link. Conventional direct sequence spread spectrum (DSSS) techniques spread the bandwidth of digital data signals over a wide frequency band by modulating them with a binary pseudonoise (PN) spreading sequence. Although the power spectral density of such a signal may be below the noise floor, the binary structure of a DSSS signal makes it vulnerable to detection, e.g., by cyclostationary signal processing techniques (as discussed in references SD1-SD3, incorporated by reference above, and SD4-SD12, which documents are hereby incorporated by reference).
Receive Issues
Rake combining is one technique that has proven to be particularly important to effective communications in restrictive environments, such as high-density urban areas, and also in dynamic scenarios (e.g. communications in the presence of moving vehicles). Due to the presence of multiple reflecting objects, a transmitted signal arrives at a receiver not only via a direct line-of-sight path, but also via multiple indirect paths. The latter so-called multipath signals are delayed and attenuated replicas of the direct signal. An important attribute of DSSS techniques is based on the fact that the spreading sequences are chosen to have autocorrelation functions that approach delta functions (i.e. impulses). Therefore, individual multipath instances of the originally transmitted signal within a received signal may reliably be located and tracked in time. This tracking capacity allows the energy from several multipath instances of the same transmitted signal to be extracted from the received signal, time-aligned, and combined coherently, thereby significantly improving the signal-to-noise ratio. (In contrast, multipath interference is extremely difficult to remove from non-DSSS communications signals and can render them undecipherable.) Rake receivers are commonly used to implement these tracking and combining functions in DSSS systems and are well understood by those of ordinary skill in the art (as discussed in reference B.9, which document is hereby incorporated by reference).
Characteristics of Noise
Background noise has a character which may change according to the particular environment in which a receiver is operating, but one component which is always present is receiver thermal noise. Such noise typically has white Gaussian statistics, in that the values of any set of samples taken from a segment of thermal noise will tend to have a normal distribution. Additionally white Gaussian noise has the following properties:
P1) Auto-correlation functions with no sidelobes
P2) Flat spectra
P3) No correlation with delayed replicas
P4) Real and imaginary parts of signal uncorrelated for all reference phases.
In order to make a communications signal look like noise and thereby blend into the thermal noise ensemble, it is desirable to design the signal to have the foregoing properties. Signals with Gaussian statistics also provide protection against some forms of advanced cyclostationary signal detection receivers (as discussed in references SD4-SD19). One way to produce a signal having Gaussian statistics from a binary-valued input is through the use of a matched pair of covering and uncovering modules. The covering module, which is located in the transmitter, acts to transform the highly detectable binary input sequences into a highly noise-like sequence (at the same sample rate) which is then smoothed, up-converted, and transmitted. The uncovering module, which is located in the receiver, reverses the transformation and converts the sampled noise-like signal into a useful approximation of the input sequence.
Conventional Block-based Techniques
Most conventional implementations of covering/uncovering module pairs are block-based, in that each block of input data is covered, transmitted, and uncovered as a discrete unit. Examples include fixed-length transform techniques such as the Fourier and discrete wavelet transform approaches (as discussed in references SD15-SD19). If the block size is sufficiently large and the distribution of the input data is sufficiently random, many such methods may produce an output having Gaussian statistics. However, care must be exercised in order to ensure that the block edges do not create a periodic feature detectable by cyclostationary detectors (as discussed in references SD4-D11). An additional vulnerability of the Fourier transform approach is that it is a known fixed-length transform that may readily be replicated by a curious interloper attempting to uncover the underlying binary signal.
Block-based covering/uncovering modules severely impact two significant receiver requirements: 1) the need for synchronization, and 2) the need to degrade as little as possible the performance of receiver rake-combining operations. For example, one conventional block-based method synthesizes the spectrum of the output signal directly from the input baseband data and then uses a discrete inverse Fourier transform to generate the corresponding block of time-domain coefficients for transmission. In this approach, the input block to the covering module represents the desired output spectrum and the output block of the covering module represents the complex values of the corresponding time-domain coefficients. The discrete direct Fourier transform which serves as the uncovering applique, however, is not shift invariant: the particular time index with which each received coefficient is associated depends on the coefficient""s place within the received block. If the receiver applies the wrong block boundaries to the received signal, the received time coefficients will become associated with the wrong time indices. In this case the result of decoding the signal will not be merely a shifted version of the transmitted data; rather, it may not resemble the transmitted data at all. Therefore, it is necessary for the pair of covering/uncovering modules to observe exactly the same block boundaries.
One way to ensure that both covering and uncovering modules adhere to the same boundary convention is for the operations of the covering and uncovering modules to be synchronized in time. Each module could utilize a local clock for this purpose, but unavoidable variations between the clocks"" frequencies would soon destroy any initial condition of synchronization between them. Unfortunately, it is also typically impossible to reliably synchronize the transmitter and receiver to a time reference outside the communications channel (i.e. within a transmitted reference channel), because changes in the environment and/or the relative positions of the transmitter, receiver, and time reference will induce unequal phase shifts in the synchronization and communications channels and thereby alter the required correspondence between them. Therefore, the necessary synchronization must be accomplished utilizing signals transmitted within the communications channel itself. This synchronization requirement places a significant added processing burden on the uncovering module and/or downstream receiver processing sections.
Various methods have been devised for achieving synchronization. These include carrier recovery loops (such as phase-locked and Costas loops), early-late gate tracking, and tau-dither tracking, among others known to those of ordinary skill in the art (as discussed in reference B.8, which document is hereby incorporated by reference). The initial stage of the synchronization operation, called acquisition, may be accomplished using time-domain cross-correlation or fast correlation methods based on the fast Fourier transform (FFT). For example, one typical digital acquisition strategy involves the periodic transmission of a unique sequence of symbols, sometimes called an acquisition sequence or synchronization preamble, which is known in advance to the receiver. The receiver looks for the preamble by continuously correlating its incoming data stream against the known sequence. Receipt of the preamble, which constitutes a synchronization event, is evidenced by the appearance of a correlation spike at the receiver. Significant additional processing hardware is required for acquisition over and above that required simply to perform the uncovering operation.
An equally serious consequence for DSSS systems is that a block-based uncovering module can fragment or destroy the nonaligned multipath signal instances upon which effective rake combining depends. In the general case, therefore, a DSSS system using such an uncovering module can forfeit a principal advantage of DSSS techniques, unless the receiver includes block processing hardware that is time-aligned with each delayed component in the signal to be combined. Obviously, such replication of hardware is undesirable for any implementation using a block large enough to ensure a signal having Gaussian statistics. As a result, the system will be unable to combine energy from different instances of the same signal, particularly in dynamic scenarios, and will become susceptible to multipath interference and distortion.
A novel method and apparatus provides a way to (1) transform a structured data sequence into a sequence that appears noise-like when observed by a curious interloper and (2) transform the noise-like sequence back into a useful version of the original structured data sequence as required by the application. The method utilizes a matched pair of programmable digital-signal-processing modules: a covering module and an uncovering module. The covering module transforms each input data sequence into a noise-like sequence having the same sample rate as the input sequence. For randomized input data and a suitably designed covering module, the resultant sequence has approximately Gaussian statistics and is extremely difficult for a third-party observer to distinguish from background noise. The uncovering module reverses the transformation, converting the noise-like sequence substantially to original form. Both the covering and uncovering modules are implemented via linear time-invariant signal processing structures. Thus, neither device requires a time reference in order to perform its function properly. The implementation of the uncovering module completely obviates the troublesome synchronization requirement of conventional block processing techniques. Additionally, the principle of superposition applies to the uncovering module; therefore, this module need not impose any performance loss on downstream rake-combining operations. The embodiments described can be programmed with a large number of discrete codes to facilitate covertness, security, and multiple access.